Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Deep research over the Semantic Scholar Graph API. An agent skill from CodeAlive-AI/ai-driven-development.
Install Semantic Scholar Deep
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deep --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/semantic-scholar-deep .claude/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .claude/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deepType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deep --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/semantic-scholar-deep .agents/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .agents/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deep --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/semantic-scholar-deep .cursor/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .cursor/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CodeAlive-AI/ai-driven-development.git --path skills/semantic-scholar-deep--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deep --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/semantic-scholar-deep .gemini/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .gemini/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deepInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/semantic-scholar-deep .github/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .github/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development semantic-scholar-deep --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/semantic-scholar-deep .opencode/skills/semantic-scholar-deep && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "semantic-scholar-deep" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/semantic-scholar-deep into .opencode/skills/semantic-scholar-deep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-scholar-deep", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
semantic-scholar-deep- GitHub stars
- 157
- Used in
- 1 other repo
- Token cost
- ~2.2k tokens
- SKILL.md length
- 834 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
Deep research over the Semantic Scholar Graph API. An agent skill from CodeAlive-AI/ai-driven-development.
- Works in 2 steps: Today's date — inline as Today is… → User's request, verbatim — pass the…
- The user asks to build a citation graph
- SKILL.md covers Contents, Dispatch Rule (read first), When to Use and Scripts, plus 5 more sections
- Runs Python scripts from its folder; calls python3; needs SEMANTIC_SCHOLAR_API_KEY
What it does
Semantic Scholar Deep is an agent skill from CodeAlive-AI/ai-driven-development. Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation graph, expand a literature seed, find related work, run a reference network traversal, explore what a paper cites or what cites it beyond simple lookup, or batch-resolve many DOI/arXiv/S2 IDs. For…
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `README.md`, `agents/deep-paper-researcher.md` and `references/endpoints.md`).
It sits in Research & Science, covering Academic paper search and Citation management. It works with Semantic Scholar and arXiv. The repository describes itself as: Practices, protocols, and skills for AI-driven software development. Skills and safety hooks for Claude Code, Codex, OpenCode, Cursor, Antigravity, and any agent supporting the… The licence is MIT.
When your agent uses it
- The user asks to build a citation graph
- Expand a literature seed
- Find related work
- Run a reference network traversal
Example prompts
- “/semantic-scholar-deep”
Requirements
- Python 3
- A credential in SEMANTIC_SCHOLAR_API_KEY
- Pre-approved tools (allowed-tools): Bash(python3:*), Read, Write, Edit, Glob, Grep, Agent
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Today's date — inline as Today is YYYY-MM-DD. Pull from the currentDate system-reminder field, or run date -I via Bash before delegating…
- User's request, verbatim — pass the user's original phrasing (topic + any freshness words like "современные / recent / классические /…
What it can do on your machine
Read from SKILL.md and the folder at commit 25b7b1d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
Bash(python3:*)ReadWriteEditGlobGrepAgent
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
semanticscholar.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
SEMANTIC_SCHOLAR_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Semantic Scholar Deep loads about 2.2k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 191 tokens; SKILL.md has 834 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from CodeAlive-AI/ai-driven-development at commit 25b7b1d, republished under its MIT licence (© CodeAlive-AI). 834 words, ~2,186 tokens.
Download SKILL.mdSave it as
.claude/skills/semantic-scholar-deep/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- semantic-scholar-deep
- description
- Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation graph, expand a literature seed, find related work, run a reference network traversal, explore what a paper cites or what cites it beyond simple lookup, or batch-resolve many DOI/arXiv/S2 IDs. For multi-step research questions, delegate to the deep-paper-researcher subagent to keep the main context clean. Not for single paper-by-ID lookups (use semantic-scholar-lookup) or topical discovery (use web_search_advanced_exa).
- allowed-tools
- Bash(python3:*), Read, Write, Edit, Glob, Grep, Agent
Semantic Scholar — Deep Research
Purpose: fill the gaps that semantic-scholar-lookup (allenai) leaves — references, recommendations, batch, and multi-hop citation-graph traversal.
Contents
- Dispatch Rule — inline vs delegate; model selection
- When to Use — trigger scenarios
- Scripts —
ss_client.py+citation_graph.py - Authentication & Rate Limits
- Progressive Disclosure — deeper references
- Output Hygiene
- Integration — typical pipeline with the subagent
Dispatch Rule (read first)
Two execution modes:
Inline (run the Bash scripts yourself)
Use when the user asks for one specific endpoint:
- "get references of paper X" →
ss_client.py references <id> - "recommendations for paper Y" →
ss_client.py recommendations <id> - "batch-resolve these 30 DOIs" →
ss_client.py batch ... - "find the snippet where X is said" →
ss_client.py snippets "..."
Fast, cheap, no orchestration overhead.
Delegate to deep-paper-researcher subagent
Use when the task is multi-step or would otherwise flood the context:
- Literature review on a topic
- Citation graph / network analysis around a seed paper
- Novelty check for an idea
- State-of-the-art survey
- Anything that requires merging Exa discovery + S2 graph + ranking
Mandatory prompt contents. The subagent runs in isolated context with no access to this conversation's system reminders. Include exactly these two things:
- Today's date — inline as
Today is YYYY-MM-DD.Pull from thecurrentDatesystem-reminder field, or rundate -Ivia Bash before delegating if it's missing. Never rely on training-data intuitions about the current year. - User's request, verbatim — pass the user's original phrasing (topic + any freshness words like "современные / recent / классические / seminal" and any explicit dates like "since 2024"). Translate language if needed but do not paraphrase trigger words into date windows.
Do NOT do any of these:
- Do NOT classify freshness yourself (RECENT/FOUNDATIONAL/MIXED). The subagent does that from the verbatim user request.
- Do NOT invent a date window. If the user said "современные / recent / latest" without a year, the subagent defaults to last 6 months — don't preempt it with "2024-2026".
- Do NOT drop the trigger words. The subagent relies on them to pick the right mode.
Call:
Agent(
subagent_type="deep-paper-researcher",
description="<3–5 word task>",
prompt="Today is 2026-04-22.\n\nUser's request: найди современные 10 статей про AI Code Review на arXiv.\n\n<optional: output format hints, language preference>"
# model: "opus" ← add only when the user opts in (see below)
)The subagent's Freshness Mode section handles classification; keep this layer thin.
Model selection (Sonnet default, Opus on demand)
The subagent's model frontmatter is sonnet — that's the default.
Override to Opus by passing model: "opus" to the Agent tool only if the user explicitly requests deeper reasoning. Triggers (any of):
- English: "deep dive", "thorough", "rigorous", "use Opus", "high quality", "comprehensive", "exhaustive"
- Russian: "глубокий/глубже", "тщательный/тщательно", "подробно", "в режиме Опус/Opus", "максимально качественно", "серьёзный ресерч"
Never auto-upgrade to Opus without a user signal — Sonnet handles the default literature-review workflow fine and costs less.
When to Use
Trigger this skill for:
- Citation graph / network over a seed paper or topic
- Backward references (what does this paper cite?) — not covered by allenai
- Forward citations with pagination beyond 1000 results
- Recommendations — related-paper discovery from a seed
- Batch lookup — resolve 50-500 DOI/arXiv/CorpusId/S2 IDs in one call
- Snippet search — find specific passages across the S2 corpus
Do NOT use for:
- Simple "get paper by ID" or "who cited this" — use
semantic-scholar-lookup(faster, no Python) - Broad topical discovery — use
web_search_advanced_exawithcategory: "research paper"(Exa MCP) - Consumer-level literature questions — use the
deep-paper-researchersubagent, which orchestrates all three tools
Show full SKILL.md (334 more words)Show less
Scripts
Located under ${SKILL_DIR}/scripts/.
ss_client.py — raw API client
Subcommands (all output JSON on stdout):
| Command | Endpoint | Notes |
|---|---|---|
search <query> | /graph/v1/paper/search | --bulk switches to /search/bulk (up to 1000/page) |
paper <id> | /graph/v1/paper/{id} | ID forms: raw, DOI:, ARXIV:, CorpusId:, PMID:, URL: |
citations <id> | /graph/v1/paper/{id}/citations | paginated; up to 1000 per page |
references <id> | /graph/v1/paper/{id}/references | paginated; up to 1000 per page |
recommendations <id> | /recommendations/v1/papers/forpaper/{id} | `--pool recent |
batch <id1> <id2> ... | POST /graph/v1/paper/batch | up to 500 IDs |
author-search <query> | /graph/v1/author/search | |
author <id> | /graph/v1/author/{id} | |
author-papers <id> | /graph/v1/author/{id}/papers | |
snippets <query> | /graph/v1/snippet/search | Full-text snippets |
Common flags: --limit, --offset, --fields, --year, --fields-of-study, --venue, --min-citation-count.
citation_graph.py — BFS traversal
python3 ${SKILL_DIR}/scripts/citation_graph.py <paperId> \
--direction both \
--depth 2 \
--max-nodes 200 \
--per-hop-limit 50 \
--output graph.jsonDirections: forward (citations), backward (references), both. Output schema described in the script docstring — nodes: {paperId → metadata+depth}, edges: [{src, dst, direction}].
Authentication & Rate Limits
- Without API key: ~1 RPS shared, 100 queries/5min bursts. Fine for small graphs.
- With
SEMANTIC_SCHOLAR_API_KEYenv var: much higher limits. - Apply: https://www.semanticscholar.org/product/api#api-key
- The client does exponential backoff (1→30s) on HTTP 429/5xx, respects
Retry-After.
Progressive Disclosure
references/endpoints.md— complete field list per endpoint + query examplesreferences/workflows.md— lit-review, novelty-check, seed-expansion patterns
Output Hygiene
Scripts emit raw JSON — redirect to files for anything beyond ~20 results. For graphs >50 nodes always pass --output graph.json to avoid flooding the conversation context.
Integration
Typical pipeline inside the deep-paper-researcher subagent:
- Discovery —
mcp__exa__web_search_advanced_exa(neural + multi-source) - ID resolution —
ss_client.py search/batchto getpaperIdfrom titles or DOIs - Graph expansion —
citation_graph.pywith the top 3-5 seeds - Synthesis — distill nodes/edges into a ranked report
Optional: Bundled Subagent
A paired subagent definition ships alongside the skill at agents/deep-paper-researcher.md. It orchestrates Exa MCP + allenai semantic-scholar-lookup + this skill's scripts into a token-isolated research agent with:
- Mandatory input validation (today's date anchoring + caller-paraphrased-window detection)
- Freshness Mode classifier (RECENT / FOUNDATIONAL / MIXED)
- Sort-then-tiebreak ranking (never multiplies citations × recency into a single score)
- Compact report format with explicit
Anchor date/Mode/Windowheader
To install for Claude Code (manual, one-time):
cp ~/.agents/skills/semantic-scholar-deep/agents/deep-paper-researcher.md ~/.claude/agents/(Path may differ on other agents — copy to the agent's subagents directory, then restart the session.)
Prerequisites for full pipeline: Exa MCP connected, allenai/asta-plugins@"Semantic Scholar Lookup" skill installed.
© CodeAlive-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (scripts, references) in skills/semantic-scholar-deep of CodeAlive-AI/ai-driven-development.
- SKILL.md
- README.md
- agents/deep-paper-researcher.md
- references/endpoints.md
- references/workflows.md
- scripts/citation_graph.py
- scripts/ss_client.py
Open the folder on GitHubat commit 25b7b1d
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in CodeAlive-AI/ai-driven-development, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Semantic Scholar Deep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Semantic Scholar Deep this skillCodeAlive-AI/ai-driven-development | 157 | 1 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 21 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Paper Research on arXivXiaomiMiMo/MiMo-Code | 14k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Literature Review AgentAr9av/PaperOrchestra | 677 | 2 repos | ~5.2k | Automated safety check: Pass | Custom licence | |
| Paper AutoratersAr9av/PaperOrchestra | 677 | 2 repos | ~1.6k | Automated safety check: Pass | Custom licence | |
| Deep Research Literature SurveyHKUSTDial/Supervisor-Skills | 8.5k | — | ~2.4k | Automated safety check: Pass | CC-BY-NC-SA-4.0 |
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Paper Research on arXiv
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Searches arXiv, fetches metadata, generates BibTeX, downloads PDFs and finds citations and related papers using a bundled Python script.
Literature Review Agent
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Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). An agent skill from Ar9av/PaperOrchestra.
Paper Autoraters
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Runs a survey-grade literature investigation: fixes the research questions, searches from adversarial angles, verifies citations and writes an evidence-first report.
Nature Academic Search
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Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion)…
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Questions about Semantic Scholar Deep
What does Semantic Scholar Deep do?
Deep research over the Semantic Scholar Graph API. An agent skill from CodeAlive-AI/ai-driven-development. Semantic Scholar Deep is an agent skill from CodeAlive-AI/ai-driven-development. Deep research over the Semantic Scholar Graph API.
When should I use Semantic Scholar Deep?
Semantic Scholar Deep fits situations like: the user asks to build a citation graph; expand a literature seed; find related work; run a reference network traversal.
How do I install Semantic Scholar Deep in Claude Code?
Run `npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a claude-code`. Or copy the skill folder (skills/semantic-scholar-deep in CodeAlive-AI/ai-driven-development) into .claude/skills/semantic-scholar-deep in your project. Claude Code loads it when a task matches its description.
How do I install Semantic Scholar Deep in Codex?
Run `npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a codex`. Or copy the skill folder (skills/semantic-scholar-deep in CodeAlive-AI/ai-driven-development) into .agents/skills/semantic-scholar-deep in your project. Codex loads it when a task matches its description.
Can I use Semantic Scholar Deep in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CodeAlive-AI/ai-driven-development --skill semantic-scholar-deep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/semantic-scholar-deep, .gemini/skills/semantic-scholar-deep, .github/skills/semantic-scholar-deep and .opencode/skills/semantic-scholar-deep in your project.
What does Semantic Scholar Deep need to run?
Going by SKILL.md and its folder, Semantic Scholar Deep needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named SEMANTIC_SCHOLAR_API_KEY. Our summary lists: Python 3; A credential in SEMANTIC_SCHOLAR_API_KEY. Its frontmatter pre-approves these tools: Bash(python3:*), Read, Write, Edit, Glob, Grep, Agent.
Does Semantic Scholar Deep access the network?
SKILL.md names 1 domain. As links in the text: semanticscholar.org. This is read from the text; nothing was executed.
Is Semantic Scholar Deep safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Semantic Scholar Deep use?
Semantic Scholar Deep is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Semantic Scholar Deep use?
About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.
What are the alternatives to Semantic Scholar Deep?
Skills that share tags, products or a category with Semantic Scholar Deep: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars), Literature Review Agent (Ar9av/PaperOrchestra, 677 stars) and Paper Autoraters (Ar9av/PaperOrchestra, 677 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Semantic Scholar Deep?
CodeAlive-AI (a GitHub organization) maintains it in CodeAlive-AI/ai-driven-development, which has 157 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 6, 2026.
Source: CodeAlive-AI/ai-driven-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.